
from pickle import UnpicklingError

import jax
import jax.numpy as jnp
import numpy as np
from flax.serialization import from_bytes
from flax.traverse_util import flatten_dict

from ..utils import logging


lowerCamelCase_    :			List[Any]    =	logging.get_logger(__name__)







def 							A__       (    lowerCamelCase  ,     lowerCamelCase    )	->						Dict:
				try:
								with open(lowerCamelCase  ,     """rb"""    ) as flax_state_f:
												UpperCamelCase_:				Tuple						=    from_bytes(lowerCamelCase  ,     flax_state_f.read()    )
				except UnpicklingError as e:
								try:
												with open(lowerCamelCase    ) as f:
																if f.read().startswith("""version"""    ):
																				raise OSError(
																				    """You seem to have cloned a repository without having git-lfs installed. Please"""
																				    """ install git-lfs and run `git lfs install` followed by `git lfs pull` in the"""
																				    """ folder you cloned."""    )
																else:
																				raise ValueError from e
								except (UnicodeDecodeError, ValueError):
												raise EnvironmentError(F'''Unable to convert {model_file} to Flax deserializable object. '''    )

				return load_flax_weights_in_pytorch_model(lowerCamelCase  ,     lowerCamelCase    )







def 							A__       (    lowerCamelCase  ,     lowerCamelCase    )	->						str:

				try:
								import torch  # noqa: F401
				except ImportError:
								logger.error(
								    """Loading Flax weights in PyTorch requires both PyTorch and Flax to be installed. Please see"""
								    """ https://pytorch.org/ and https://flax.readthedocs.io/en/latest/installation.html for installation"""
								    """ instructions."""    )
								raise

				# check if we have bf16 weights
				UpperCamelCase_:				int						=    flatten_dict(jax.tree_util.tree_map(lambda lowerCamelCase    : x.dtype == jnp.bfloataa  ,     lowerCamelCase    )    ).values()
				if any(lowerCamelCase    ):
								# convert all weights to fp32 if they are bf16 since torch.from_numpy can-not handle bf16

								# and bf16 is not fully supported in PT yet.
								logger.warning(
								    """Found ``bfloat16`` weights in Flax model. Casting all ``bfloat16`` weights to ``float32`` """
								    """before loading those in PyTorch model."""    )
								UpperCamelCase_:				Optional[int]						=    jax.tree_util.tree_map(
								    lambda lowerCamelCase    : params.astype(np.floataa    ) if params.dtype == jnp.bfloataa else params  ,     lowerCamelCase    )

				UpperCamelCase_:				List[str]						=    """"""

				UpperCamelCase_:				Optional[Any]						=    flatten_dict(lowerCamelCase  ,     sep="""."""    )
				UpperCamelCase_:				List[str]						=    pt_model.state_dict()

				# keep track of unexpected & missing keys
				UpperCamelCase_:				int						=    []
				UpperCamelCase_:				List[str]						=    set(pt_model_dict.keys()    )

				for flax_key_tuple, flax_tensor in flax_state_dict.items():
								UpperCamelCase_:				Tuple						=    flax_key_tuple.split("""."""    )

								if flax_key_tuple_array[-1] == "kernel" and flax_tensor.ndim == 4:
												UpperCamelCase_:				str						=    flax_key_tuple_array[:-1] + ["""weight"""]
												UpperCamelCase_:				Any						=    jnp.transpose(lowerCamelCase  ,     (3, 2, 0, 1)    )
								elif flax_key_tuple_array[-1] == "kernel":
												UpperCamelCase_:				Tuple						=    flax_key_tuple_array[:-1] + ["""weight"""]
												UpperCamelCase_:				int						=    flax_tensor.T
								elif flax_key_tuple_array[-1] == "scale":
												UpperCamelCase_:				Any						=    flax_key_tuple_array[:-1] + ["""weight"""]

								if "time_embedding" not in flax_key_tuple_array:
												for i, flax_key_tuple_string in enumerate(lowerCamelCase    ):
																UpperCamelCase_:				Dict						=    (
																    flax_key_tuple_string.replace("""_0"""  ,     """.0"""    )
																    .replace("""_1"""  ,     """.1"""    )
																    .replace("""_2"""  ,     """.2"""    )
																    .replace("""_3"""  ,     """.3"""    )
																    .replace("""_4"""  ,     """.4"""    )
																    .replace("""_5"""  ,     """.5"""    )
																    .replace("""_6"""  ,     """.6"""    )
																    .replace("""_7"""  ,     """.7"""    )
																    .replace("""_8"""  ,     """.8"""    )
																    .replace("""_9"""  ,     """.9"""    )
																)

								UpperCamelCase_:				Tuple						=    """.""".join(lowerCamelCase    )

								if flax_key in pt_model_dict:
												if flax_tensor.shape != pt_model_dict[flax_key].shape:
																raise ValueError(
																    F'''Flax checkpoint seems to be incorrect. Weight {flax_key_tuple} was expected '''
																    F'''to be of shape {pt_model_dict[flax_key].shape}, but is {flax_tensor.shape}.'''    )
												else:
																# add weight to pytorch dict
																UpperCamelCase_:				Union[str, Any]						=    np.asarray(lowerCamelCase    ) if not isinstance(lowerCamelCase  ,     np.ndarray    ) else flax_tensor
																UpperCamelCase_:				Dict						=    torch.from_numpy(lowerCamelCase    )
																# remove from missing keys
																missing_keys.remove(lowerCamelCase    )
								else:
												# weight is not expected by PyTorch model
												unexpected_keys.append(lowerCamelCase    )

				pt_model.load_state_dict(lowerCamelCase    )

				# re-transform missing_keys to list
				UpperCamelCase_:				int						=    list(lowerCamelCase    )

				if len(lowerCamelCase    ) > 0:
								logger.warning(
								    """Some weights of the Flax model were not used when initializing the PyTorch model"""
								    F''' {pt_model.__class__.__name__}: {unexpected_keys}\n- This IS expected if you are initializing'''
								    F''' {pt_model.__class__.__name__} from a Flax model trained on another task or with another architecture'''
								    """ (e.g. initializing a BertForSequenceClassification model from a FlaxBertForPreTraining model).\n- This"""
								    F''' IS NOT expected if you are initializing {pt_model.__class__.__name__} from a Flax model that you expect'''
								    """ to be exactly identical (e.g. initializing a BertForSequenceClassification model from a"""
								    """ FlaxBertForSequenceClassification model)."""    )
				if len(lowerCamelCase    ) > 0:
								logger.warning(
								    F'''Some weights of {pt_model.__class__.__name__} were not initialized from the Flax model and are newly'''
								    F''' initialized: {missing_keys}\nYou should probably TRAIN this model on a down-stream task to be able to'''
								    """ use it for predictions and inference."""    )

				return pt_model








import torch

from diffusers import CMStochasticIterativeScheduler

from .test_schedulers import SchedulerCommonTest

class 							_UpperCamelCase					(       _A	):






			'''simple docstring'''

			__UpperCamelCase   :    Tuple          =			(CMStochasticIterativeScheduler,)
			__UpperCamelCase   :    List[str]          =			10

			def    lowerCAmelCase__						(							self			:							Optional[int]    ,						**snake_case_			:							List[str]   ):
							UpperCamelCase_:				Optional[int]						=    {
							    """num_train_timesteps""": 201,
							    """sigma_min""": 0.002,
							    """sigma_max""": 80.0,
							}

							config.update(**snake_case_   )
							return config

			def    lowerCAmelCase__						(							self			:							Tuple   ):
							UpperCamelCase_:				Tuple						=    10

							UpperCamelCase_:				Tuple						=    self.get_scheduler_config()
							UpperCamelCase_:				List[str]						=    self.scheduler_classes[0](**snake_case_   )

							scheduler.set_timesteps(snake_case_   )

							UpperCamelCase_:				int						=    scheduler.timesteps[0]
							UpperCamelCase_:				str						=    scheduler.timesteps[1]

							UpperCamelCase_:				Optional[int]						=    self.dummy_sample
							UpperCamelCase_:				int						=    0.1 * sample

							UpperCamelCase_:				Optional[Any]						=    scheduler.step(snake_case_    ,						snake_case_    ,						snake_case_   ).prev_sample
							UpperCamelCase_:				Any						=    scheduler.step(snake_case_    ,						snake_case_    ,						snake_case_   ).prev_sample

							self.assertEqual(output_a.shape    ,						sample.shape   )
							self.assertEqual(output_a.shape    ,						output_a.shape   )

			def    lowerCAmelCase__						(							self			:							Optional[Any]   ):
							for timesteps in [10, 50, 100, 1000]:
											self.check_over_configs(num_train_timesteps=snake_case_   )

			def    lowerCAmelCase__						(							self			:							Optional[Any]   ):
							for clip_denoised in [True, False]:
											self.check_over_configs(clip_denoised=snake_case_   )

			def    lowerCAmelCase__						(							self			:							Any   ):
							UpperCamelCase_:				Any						=    self.scheduler_classes[0]
							UpperCamelCase_:				List[str]						=    self.get_scheduler_config()
							UpperCamelCase_:				Tuple						=    scheduler_class(**snake_case_   )

							UpperCamelCase_:				Optional[int]						=    1
							scheduler.set_timesteps(snake_case_   )
							UpperCamelCase_:				Any						=    scheduler.timesteps

							UpperCamelCase_:				int						=    torch.manual_seed(0   )

							UpperCamelCase_:				Any						=    self.dummy_model()
							UpperCamelCase_:				Union[str, Any]						=    self.dummy_sample_deter * scheduler.init_noise_sigma

							for i, t in enumerate(snake_case_   ):
											# 1. scale model input
											UpperCamelCase_:				str						=    scheduler.scale_model_input(snake_case_    ,						snake_case_   )

											# 2. predict noise residual
											UpperCamelCase_:				List[Any]						=    model(snake_case_    ,						snake_case_   )

											# 3. predict previous sample x_t-1
											UpperCamelCase_:				str						=    scheduler.step(snake_case_    ,						snake_case_    ,						snake_case_    ,						generator=snake_case_   ).prev_sample

											UpperCamelCase_:				Union[str, Any]						=    pred_prev_sample

							UpperCamelCase_:				int						=    torch.sum(torch.abs(snake_case_   )   )
							UpperCamelCase_:				List[Any]						=    torch.mean(torch.abs(snake_case_   )   )

							assert abs(result_sum.item() - 192.7614   ) < 1e-2
							assert abs(result_mean.item() - 0.2510   ) < 1e-3

			def    lowerCAmelCase__						(							self			:							str   ):
							UpperCamelCase_:				Dict						=    self.scheduler_classes[0]
							UpperCamelCase_:				Tuple						=    self.get_scheduler_config()
							UpperCamelCase_:				List[Any]						=    scheduler_class(**snake_case_   )

							UpperCamelCase_:				str						=    [106, 0]
							scheduler.set_timesteps(timesteps=snake_case_   )
							UpperCamelCase_:				int						=    scheduler.timesteps

							UpperCamelCase_:				List[Any]						=    torch.manual_seed(0   )

							UpperCamelCase_:				List[Any]						=    self.dummy_model()
							UpperCamelCase_:				int						=    self.dummy_sample_deter * scheduler.init_noise_sigma

							for t in timesteps:
											# 1. scale model input
											UpperCamelCase_:				Optional[int]						=    scheduler.scale_model_input(snake_case_    ,						snake_case_   )

											# 2. predict noise residual
											UpperCamelCase_:				Dict						=    model(snake_case_    ,						snake_case_   )

											# 3. predict previous sample x_t-1
											UpperCamelCase_:				str						=    scheduler.step(snake_case_    ,						snake_case_    ,						snake_case_    ,						generator=snake_case_   ).prev_sample

											UpperCamelCase_:				List[str]						=    pred_prev_sample

							UpperCamelCase_:				Union[str, Any]						=    torch.sum(torch.abs(snake_case_   )   )
							UpperCamelCase_:				str						=    torch.mean(torch.abs(snake_case_   )   )

							assert abs(result_sum.item() - 347.6357   ) < 1e-2
							assert abs(result_mean.item() - 0.4527   ) < 1e-3

			def    lowerCAmelCase__						(							self			:							str   ):
							UpperCamelCase_:				int						=    self.scheduler_classes[0]
							UpperCamelCase_:				Optional[int]						=    self.get_scheduler_config()
							UpperCamelCase_:				Tuple						=    scheduler_class(**snake_case_   )

							UpperCamelCase_:				Any						=    [39, 30, 12, 15, 0]

							with self.assertRaises(snake_case_    ,						msg="""`timesteps` must be in descending order."""   ):
											scheduler.set_timesteps(timesteps=snake_case_   )

			def    lowerCAmelCase__						(							self			:							str   ):
							UpperCamelCase_:				Union[str, Any]						=    self.scheduler_classes[0]
							UpperCamelCase_:				Tuple						=    self.get_scheduler_config()
							UpperCamelCase_:				List[str]						=    scheduler_class(**snake_case_   )

							UpperCamelCase_:				Tuple						=    [39, 30, 12, 1, 0]
							UpperCamelCase_:				List[Any]						=    len(snake_case_   )

							with self.assertRaises(snake_case_    ,						msg="""Can only pass one of `num_inference_steps` or `timesteps`."""   ):
											scheduler.set_timesteps(num_inference_steps=snake_case_    ,						timesteps=snake_case_   )




			def    lowerCAmelCase__						(							self			:							int   ):
							UpperCamelCase_:				Any						=    self.scheduler_classes[0]
							UpperCamelCase_:				int						=    self.get_scheduler_config()
							UpperCamelCase_:				int						=    scheduler_class(**snake_case_   )

							UpperCamelCase_:				int						=    [scheduler.config.num_train_timesteps]

							with self.assertRaises(
							    snake_case_    ,						msg="""`timesteps` must start before `self.config.train_timesteps`: {scheduler.config.num_train_timesteps}}"""    ,						):
											scheduler.set_timesteps(timesteps=snake_case_   )






